Adaptation to conflict frequency without contingency and temporal learning: Evidence from the picture–word interference task.
Bibliographic record
Abstract
In interference tasks (e.g., Stroop, 1935), the difference between congruent and incongruent latencies (i.e., the "congruency" effect) is larger in trial blocks containing mostly congruent trials than in trial blocks containing mostly incongruent trials (the proportion-congruent [PC] effect). Although the PC effect has typically been interpreted as reflecting adjustments in attention toward/away from the task-irrelevant dimension (i.e., a conflict-adaptation strategy), recent research has suggested alternative accounts based on the learning of either contingencies (i.e., distractor-response associations) or of temporal expectancies (i.e., the typical response speed on previous trials), accounts in which conflict adaptation plays no role. Using the picture-word interference paradigm, we report data from two PC manipulations in which contingency learning was made impossible by using nonrepeated distractors (Experiment 1A) or both nonrepeated distractors and responses (Experiment 1B). The classic PC effect emerged in both experiments. In addition, learning of temporal expectancies could not explain the present PC effects either, as results from trial-level analyses of Experiments 1A and 1B and a nonconflict version of Experiment 1B (Experiment 2) were inconsistent with the predictions of the temporal learning account of PC effects. These results suggest that conflict adaptation remains a credible explanation for PC effects. (PsycINFO Database Record (c) 2019 APA, all rights reserved).
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".